AI RESEARCH
AerialVLA: A Vision-Language-Action Model for UAV Navigation via Minimalist End-to-End Control
arXiv CS.AI
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ArXi:2603.14363v1 Announce Type: cross Vision-Language Navigation (VLN) for Unmanned Aerial Vehicles (UAVs) demands complex visual interpretation and continuous control in dynamic 3D environments. Existing hierarchical approaches rely on dense oracle guidance or auxiliary object detectors, creating semantic gaps and limiting genuine autonomy. We propose AerialVLA, a minimalist end-to-end Vision-Language-Action framework mapping raw visual observations and fuzzy linguistic instructions directly to continuous physical control signals. First, we.